{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:00:44.931667400Z",
     "start_time": "2023-06-15T07:00:44.438207300Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "import func\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "rc = {'font.sans-serif': 'SimHei',\n",
    "      'axes.unicode_minus': False}\n",
    "sns.set_theme(style=\"white\", context=\"talk\", font_scale=0.75, rc=rc)\n",
    "\n",
    "FILE_PATH = \"../dataset/satisfaction_v2.csv\"\n",
    "PICTURE_PATH = \"../picture/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "#数据集读取与分离\n",
    "data = func.get_pre_processed_data_set(FILE_PATH)\n",
    "data_x, data_y = func.x_y_split(data, \"satisfaction_v2\")\n",
    "train_data, test_data, dev_data = func.split_data(data)\n",
    "train_x, train_y = func.x_y_split(train_data, \"satisfaction_v2\")\n",
    "test_x, test_y = func.x_y_split(test_data, \"satisfaction_v2\")\n",
    "dev_x, dev_y = func.x_y_split(dev_data, \"satisfaction_v2\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:00:45.507490700Z",
     "start_time": "2023-06-15T07:00:44.932665300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归测试集准确率：0.7954282183952429\n"
     ]
    }
   ],
   "source": [
    "#逻辑回归\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "def logistic_regression(train_x, train_y, test_x, test_y):\n",
    "    model = LogisticRegression()\n",
    "    model.fit(train_x, train_y)\n",
    "    y_score = pd.DataFrame(model.predict_proba(test_x)).iloc[:, 1]\n",
    "    y_score.index = test_y.index\n",
    "    return y_score, accuracy_score(model.predict(test_x), test_y)\n",
    "\n",
    "\n",
    "y_score, accuracy = logistic_regression(train_x, train_y, test_x, test_y)  #y_score为类别为1的置信度\n",
    "print(f\"逻辑回归测试集准确率：{accuracy}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:00:46.252260500Z",
     "start_time": "2023-06-15T07:00:45.508528500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.make_P_R_ROC(y_score, test_y, P_R_title=\"PR-逻辑回归\", ROC_title=\"ROC-逻辑回归\", filename=PICTURE_PATH + \"P_R&ROC_逻辑回归.png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:00:50.511175100Z",
     "start_time": "2023-06-15T07:00:46.255252600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林测试集准确率：0.9653255077612171\n"
     ]
    }
   ],
   "source": [
    "#随机森林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def randomforest_classifier(train_x, train_y, test_x, test_y):\n",
    "    model = RandomForestClassifier(n_estimators=200)\n",
    "    model.fit(train_x, train_y)\n",
    "    y_score = pd.DataFrame(model.predict_proba(test_x)).iloc[:, 1]\n",
    "    y_score.index = test_y.index\n",
    "    return y_score, accuracy_score(model.predict(test_x), test_y)\n",
    "\n",
    "\n",
    "y_score, accuracy = randomforest_classifier(train_x, train_y, test_x, test_y)\n",
    "print(f\"随机森林测试集准确率：{accuracy}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:17.284245400Z",
     "start_time": "2023-06-15T07:00:50.513171300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.make_P_R_ROC(y_score, test_y, P_R_title=\"PR-随机森林\", ROC_title=\"ROC-随机森林\", filename=PICTURE_PATH + \"P_R&ROC_随机森林.png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:22.057263100Z",
     "start_time": "2023-06-15T07:01:17.277718900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "(id\n 103914    0.998220\n 103915    0.042201\n 103916    0.999800\n 103917    0.999766\n 103918    0.999949\n             ...   \n 116892    0.999999\n 116893    0.999696\n 116894    0.985981\n 116895    1.000000\n 116896    0.030015\n Name: 1, Length: 12949, dtype: float32,\n 0.9643987952737663)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xgboost\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "\n",
    "def xgboost_regression(train_x, train_y, test_x, test_y):\n",
    "    model_xgb = XGBClassifier()\n",
    "    model_xgb.fit(train_x, train_y)\n",
    "    y_score = pd.DataFrame(model_xgb.predict_proba(test_x)).iloc[:, 1]\n",
    "    y_score.index = test_y.index\n",
    "    return y_score, accuracy_score(model_xgb.predict(test_x), test_y)\n",
    "\n",
    "y_score, accuracy = xgboost_regression(train_x, train_y, test_x, test_y)\n",
    "y_score, accuracy"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:25.637209300Z",
     "start_time": "2023-06-15T07:01:22.058260400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.make_P_R_ROC(y_score, test_y, P_R_title=\"PR-xgboost\", ROC_title=\"ROC-xgboost\", filename=PICTURE_PATH + \"P_R&ROC_xgboost.png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:30.191911300Z",
     "start_time": "2023-06-15T07:01:25.638206Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "(id\n 103914    1.000000\n 103915    0.000000\n 103916    1.000000\n 103917    0.333333\n 103918    0.666667\n             ...   \n 116892    1.000000\n 116893    1.000000\n 116894    1.000000\n 116895    1.000000\n 116896    0.333333\n Name: 1, Length: 12949, dtype: float64,\n 0.702834195690787)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN模型\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "def KNeighborsClassifier_regression(train_x, train_y, test_x, test_y):\n",
    "    knn = KNeighborsClassifier(n_neighbors=3)\n",
    "    knn.fit(train_x, train_y)\n",
    "    y_score = pd.DataFrame(knn.predict_proba(test_x)).iloc[:, 1]\n",
    "    y_score.index = test_y.index\n",
    "    return y_score, accuracy_score(knn.predict(test_x), test_y)\n",
    "\n",
    "\n",
    "y_score, accuracy = KNeighborsClassifier_regression(train_x, train_y, test_x, test_y)\n",
    "y_score, accuracy"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:33.564401900Z",
     "start_time": "2023-06-15T07:01:30.193906400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.make_P_R_ROC(y_score, test_y, P_R_title=\"PR-KNN\", ROC_title=\"ROC-KNN\", filename=PICTURE_PATH + \"P_R&ROC_KNN.png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:37.646907500Z",
     "start_time": "2023-06-15T07:01:33.566394200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "(id\n 103914    1.0\n 103915    0.0\n 103916    1.0\n 103917    1.0\n 103918    1.0\n          ... \n 116892    1.0\n 116893    1.0\n 116894    1.0\n 116895    1.0\n 116896    0.0\n Name: 1, Length: 12949, dtype: float64,\n 0.9475635184184107)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#决策树分类器\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "def DecisionTreeClassifier_regression(train_x, train_y, test_x, test_y):\n",
    "    dt = DecisionTreeClassifier()\n",
    "    dt.fit(train_x, train_y)\n",
    "    y_score = pd.DataFrame(dt.predict_proba(test_x)).iloc[:, 1]\n",
    "    y_score.index = test_y.index\n",
    "    return y_score, accuracy_score(dt.predict(test_x), test_y)\n",
    "\n",
    "\n",
    "y_score, accuracy = DecisionTreeClassifier_regression(train_x, train_y, test_x, test_y)\n",
    "y_score, accuracy"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:38.346619Z",
     "start_time": "2023-06-15T07:01:37.647904100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func.make_P_R_ROC(y_score, test_y, P_R_title=\"PR-决策树\", ROC_title=\"ROC-决策树\", filename=PICTURE_PATH + \"P_R&ROC_决策树.png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T07:01:42.496491Z",
     "start_time": "2023-06-15T07:01:38.345622500Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
